Retrieval Evaluation Measures that Agree with Users' SERP Preferences: Traditional, Preference-based, and Diversity Measures

Tetsuya Sakai, Zhaohao Zeng

Research output: Contribution to journalArticlepeer-review

Abstract

We examine the "goodness"of ranked retrieval evaluation measures in terms of how well they align with users' Search Engine Result Page (SERP) preferences for web search. The SERP preferences cover 1,127 topic-SERP-SERP triplets extracted from the NTCIR-9 INTENT task, reflecting the views of 15 different assessors. Each assessor made two SERP preference judgements for each triplet: one in terms of relevance and the other in terms of diversity. For each evaluation measure, we compute the Agreement Rate (AR) of each triplet: The proportion of assessors that agree with the measure's SERP preference. We then compare the mean ARs of the measures as well as those of best/median/worst assessors using Tukey HSD tests. Our first experiment compares traditional ranked retrieval measures based on the SERP relevance preferences: we find that normalised Discounted Cumulative Gain (nDCG) and intentwise Rank-biased Utility (iRBU) perform best in that they are the only measures that are statistically indistinguishable from our best assessor; nDCG also statistically significantly outperforms our median assessor. Our second experiment utilises 119,646 document preferences that we collected for a subset of the above topic-SERP-SERP triplets (containing 894 triplets) to compare preference-based evaluation measures as well as traditional ones. Again, we evaluate them based on the SERP relevance preferences. The results suggest that measures such as wpref5 are the most promising among the preference-based measures considered, although they underperform the best traditional measures such as nDCG on average. Our third experiment compares diversified search measures based on the SERP diversity preferences as well as the SERP relevance preferences, and it shows that D-measures are clearly the most reliable: in particular, D-nDCG and D-RBP statistically significantly outperform the median assessor and all intent-Aware measures; they also outperform the recently proposed RBU on average. Also, in terms of agreement with SERP diversity preferences, D-nDCG statistically significantly outperforms RBU. Hence, if IR researchers want to use evaluation measures that align well with users' SERP preferences, then we recommend nDCG and iRBU for traditional search, and D-measures such as D-nDCG for diversified search. As for document preference-based measures that we have examined, we do not have a strong reason to recommended them over traditional measures like nDCG, since they align slightly less well with users' SERP preferences despite their quadratic assessment cost.

Original languageEnglish
Article number14
JournalACM Transactions on Information Systems
Volume39
Issue number2
DOIs
Publication statusPublished - 2021 Mar

Keywords

  • Document preferences
  • SERP preferences
  • evaluation measures
  • preference assessments
  • search engine result pages
  • search result diversification

ASJC Scopus subject areas

  • Information Systems
  • Business, Management and Accounting(all)
  • Computer Science Applications

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